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Quantum computing sounds like heavy jargon, yet its main applications lie in solving problems that are conceptually simple but computationally hard for classical computers.

To understand this, let’s take an example of finding the factors of a number, say 15:

15 = X * Y

Classical computer:

2 * 1, 2 * 2 … 2 * 7 = 14 → No match found
3 * 1, 3 * 2 … 3 * 5 = 15 → Match found

15 = 3 * 5

Compute Complexity: O(√N)

What can a Quantum Computer do?

There is an algorithm called Shor’s algorithm, which computes things slightly differently:

Shor's Algorithm : a^x mod N (1 < a < N)

2^x mod 15

 x = 0 → 2^0 = 1, 2^0 mod 15 = 1
 x = 1 → 2^1 = 2, 2^1 mod 15 = 2
 x = 2 → 2^2 = 4, 2^2 mod 15 = 4
 x = 3 → 2^3 = 8, 2^3 mod 15 = 8
 x = 4 → 2^4 = 16, 2^4 mod 15 = 1
 x = 5 → 2^5 = 32, 2^5 mod 15 = 2
 x = 6 → 2^6 = 64, 2^6 mod 15 = 4
 x = 7 → 2^7 = 128, 2^7 mod 15 = 8
 x = 8 → 2^8 = 256, 2^8 mod 15 = 1

If you see the pattern: 1, 2, 4, 8 → r = 4.

Factors of 15 = gcd(a^(r/2 ± 1), N) = gcd(2^(4/2 ± 1), 15) = gcd(2² ± 1, 15) = gcd(3,15) and gcd(5,15) = 3 and 5.

So, factors of 15 are 3 and 5.

A Quantum Computer can find this repeating pattern (r = 4) in one go using QFT.

QFT stands for Quantum Fourier Transform (it is similar to the normal Fourier Transform, where we represent a polynomial as a combination of sine and cosine functions and reduce it with the help of Euler’s (e) function; this is just the quantum version).

This is physically implemented in the chip, like the ALU in a conventional computer, which helps perform operations like +, −, ×, ÷.

Total qubits required (derived using QFT) = 2 * (4) + 3

What you need to do is transform every mathematical and computing problem into a form suitable for Shor’s algorithm—for example, integer factorization—to gain the computational advantages of a quantum computer.

If we take a bigger number, say 400 digits (RSA-1024 encryption context):

400 digits → 400 * log₂(10) ≈ 1328 qubits

Classical computation:

Normal computer = 3 × 10^17 years (impossible)
Supercomputer (1 million cores) = 1 billion years

Quantum computation:

Q-bits required = 2 * (1328) + 3 = 2,659 q-bits

1 gate operation per 10 microseconds → ~6.5 hours (including errors: 1–3 days)


This means it can break encryption within a few days to a few weeks.

Quantum computing may not address the fundamental philosophical questions of science, but it can tackle many critical computational challenges. It can accelerate machine learning by speeding up model training and enhancing data clustering, advance healthcare through faster drug discovery and more effective cancer research, enable precise molecular simulations and the design of novel materials, and optimise climate modelling, battery efficiency, renewable energy storage, and power grid management.
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Yash Pratap Author

Web Dev, System Design, AI
When it comes to understanding the factors responsible for poor AQI in Delhi NCR, the philosophy of science is far more important than experimental science (computing is subdomain) because, in this case, if we were to follow traditional methods and gather AQI data for Delhi NCR using sensors instead of relying on data from nearby towns like Alwar, it would require enormous investments and equipment, yet we would likely reach similar conclusions.

https://www.linkedin.com/posts/yashp2411_delhi-pollution-activity-7393295779978940416-2JEY
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Yash Pratap Author

Web Dev, System Design, AI
(edited)
Aditya Yadav Inventor of the production-grade quantum computer cracked a 5-digit RSA number using 70 logical qubits and 1.15 million gates. See the attached image below to see how it really works.

Doing drug discovery and cryptography requires hundreds of millions if not billions of gates. And IBM will only be doing 15000 by end of 2028. We can say that IBMs quantum computer is of no consequence. - Aditya Yadav

Real qubits aren’t error-free, and we need error-free (logical) qubits, which means we need at least 100,000 to 1 million qubits to make anything work in a practical sense, and we’re still very far from that.
Comment image, no alternative text available
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Rp Singh • 1st

Director at New Delhi DataPoint Pvt. Ltd. working in the area of Data Science ML AI and IOT and Robotics
Solve any problem is to know the problem itself , does solution needs speed. As speed reduces the depth and understanding meaning or purpose, actually the problem don't touch the root of problem.
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Srinivas Kumar • Following

Entrepreneur & Technocrat (R&D Driven Innovations)
Vedic math teaches pattern recognition and parallel processing - similar to quantum theory. There is beauty and symmetry in numbers as the basic units - the super scentific calculator of the Gods and mysteries that Ramanujan could decode. Kaprekar’s constant is one example of the wonders hidden in mathematics. Looking forward to future generations exploring Vedic math and applying it to solve real world problems with low compute power/energy.
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Yash Pratap Author

Web Dev, System Design, AI
Srinivas Kumar unable to understand what exactly resembles to Vedic maths. Aryabhatta develop mathematics in 400CE prior to him we didn't had good enough methods to do decimals and Vedas are 1000BC old. I'm not sure how can you associate Vedas with mathematics.
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Srinivas Kumar • Following

Entrepreneur & Technocrat (R&D Driven Innovations)
Quantum theory emphasizes interconnectedness of quantum states. Vedic mathematics treats numbers as interconnected. Quantum arithmetic circuits could be optimized using Urdhva-Tiryagbhyam for quantum multiplication gates. Interesting research articles to refer to: Synthesis of a reversible quantum Vedic multiplier on IBM quantum computers, Implementation of Vedic Multiplier Using reversible Gates, Design of High Performance ALU Using Vedic Mathematics.
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Srinivas Kumar • Following

Entrepreneur & Technocrat (R&D Driven Innovations)
I am neither a mathematician nor cryptographer myself. I am quoting published articles by professionals in this field.

The basic process for two-digit numbers is as follows:
Step 1: Multiply vertically.
Multiply the rightmost digits together. For example, in 24×25 you would multiply 4×5=20. Write down the 0 and carry over the 2.
Step 2: Multiply crosswise and add.
Multiply the digits diagonally and add the products. For 24×25. This would be (2×5)+(4×2)=10+8=18. Add the carry-over from the previous step: 18+2=20. Write down the 0 and carry over the 2.
Step 3: Multiply vertically.
Multiply the leftmost digits together. For 24×25 this would be 2×2=4.Add the carry-over from the previous step 4+2=6.Write this down as the final result.
The final answer: Combining the digits from right to left gives the answer: 600.

Extending the method:
For larger numbers: The process is extended for three or more digits by performing a series of vertical and cross multiplications, moving from the rightmost digits to the leftmost.
For long division: The converse of this method can also be used for long division.

https://verilogbeginner.wordpress.com/2018/12/08/binary-multiplier-using-vedic-mathematics-urdhva-tiryagbhyam-method/
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Srinivas Kumar • Following

Entrepreneur & Technocrat (R&D Driven Innovations)
With quantum computers, public key cryptography is at risk because factoring large numbers into primes could take only hours. Classical key exchange protocols based on integer factorization (DH) or the discrete logarithm problem (elliptic curve DH - ECDH) may be broken by QC in polynomial time. Peter Shor showed how to factor a number using quantum computers with the effect of reducing the time required from years down to hours. Shor's algorithm can be used to target asymmetric keys, which are the basis for the PKI. If Shor's algorithm ever becomes practical, then any existing keys and data that are stored anywhere need to be re-encrypted. Even if quantum computers turn out to be much less expensive than anticipated, the known difficulty of parallelizing Lov Grover's algorithm suggests that both AES 192 and AES 256 will still be safe for a very long time.
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Srinivas Kumar • Following

Entrepreneur & Technocrat (R&D Driven Innovations)
The difference in spirit:
Lattice methods are formal, high-dimensional, and complexity-theoretic—used to build secure post-quantum cryptography.
Vedic mathematics is heuristic, low-dimensional, and mental—used for rapid human arithmetic.
The parallel lies in how structure and pattern are exploited to make difficult calculations manageable.

A poetic synthesis:
Vedic mathematics is a “human lattice” of numerical intuition—where numbers align along hidden grids of base and complement—while lattice cryptography formalizes these grids into the abstract algebraic spaces of security.
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Sharma RS • 1st

A Visionary & Committed Leader (BJP & NDA Supporter) || ED, Manas Foundation (NGO) || In Favour of Recreation of Small States & UT on the Basis of States Reorganisation Commission Act of 1956 throughout India.
Shor's algorithm is a quantum method to quickly find the factors of large numbers, which is very slow for classical computers.

It works by transforming factoring into a problem of finding a hidden repeating pattern (period) using Quantum Fourier Transform (QFT).

This lets a quantum computer find factors in polynomial time, making it powerful enough to break current encryption methods that classical computers can't crack efficiently.

Simply put, Shor's algorithm uses quantum mechanics to find factors much faster than traditional methods